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A New Methodology to Select the Preferred Solutions from the Pareto-optimal Set: Application to Polymer Extrusion

机译:一种新的方法,可以从帕累托 - 最佳集合中选择优选的解决方案:适用于聚合物挤出

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摘要

Most of the real world optimization problems involve multiple, usually conflicting, optimization criteria. Generating Pareto optimal solutions plays an important role in multi-objective optimization, and the problem is considered to be solved when the Pareto optimal set is found, i.e., the set of non-dominated solutions. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to produce the complete set of non-dominated solutions. However, this is not allays enough since the aim is not only to know the Pareto set but, also, to obtain one solution from this Pareto set. Thus, the definition of a methodology able to select a single solution from the set of non-dominated solutions (or a region of the Pareto frontier), and taking into account the preferences of a Decision Maker (DM), is necessary. A different method, based on a weighted stress function, is proposed. It is able to integrate the user's preferences in order to find the best region of the Pareto frontier accordingly with these preferences. This method was tested on some benchmark test problems, with two and three criteria, and on a polymer extrusion problem. This methodology is able to select efficiently the best Pareto-frontier region for the specified relative importance of the criteria.
机译:大多数现实世界优化问题涉及多个,通常相互冲突的优化标准。生成Pareto最佳解决方案在多目标优化中起着重要作用,并且当找到帕累托最佳集合时,该问题被认为是解决的,即,这组非主导解决方案。基于Pareto最优性原理的多目标进化算法旨在生产完整的非主导解决方案。然而,这不是足够的,因为目的不仅要知道帕累托集,而且还可以从这个帕累托集中获得一个解决方案。因此,能够从该组非主导的解决方案(或帕累托前沿的区域)中选择单个解决方案的方法的定义,并考虑决策者(DM)的偏好。提出了一种基于加权应力函数的不同方法。它能够集成用户的偏好,以便通过这些偏好找到Paroto边防的最佳区域。在一些基准测试问题上测试了该方法,具有两个和三个标准,以及聚合物挤出问题。该方法能够为标准的指定相对重要性提供有效的静态前沿区域。

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